ETL Developer

Job Description:
1. Minimum 15 years of experience in Data Engineering with proven expertise in Talend as the primary ETL tool for enterprise data integration and migration.
2. Extensive experience in data migration from legacy sources including MS Access, Excel, flat files, Kbase, and other proprietary systems into modern data warehouses and cloud platforms.
3. Strong background in data ingestion strategies-batch ingestion, near real-time, streaming pipelines - with ability to design ingestion frameworks for structured, semi-structured, and unstructured data.
4. Expertise in Data modeling(conceptual, logical, physical), dimensional modeling (star/snowflake), and database design principles** tailored for healthcare, financial, or large-scale transactional systems.
5. Skilled in building and optimizing data warehouses, data marts, and staging layers, ensuring efficient transformations and high query performance.
6. Hands-on experience with Talend ingestion pipelines, job orchestration, and monitoring via Talend Administration Center (TAC) or external schedulers.
7. Proficient in SQL, stored procedures, indexing, and query optimization** with strong experience on SQL Server, Oracle, and other major RDBMS platforms.
8. Deep understanding of ETL/ELT lifecycle design, including data mapping, lineage, transformation rules, reconciliation, and error-handling frameworks.
9. Experience designing data ingestion frameworks with scalability in mind, supporting both on-premise and cloud data platforms(Azure, AWS, GCP, Snowflake).
10. Strong knowledge of data quality frameworks, profiling, validation checks, and reconciliation to maintain trusted datasets for business analytics.
11. Ability to design secure ingestion pipelines with robust error recovery, logging, auditing, and monitoring mechanisms.
12. Exposure to cloud-native data ingestion services (Azure Data Factory, AWS Glue, GCP Dataflow) integrated with Talend for hybrid migration solutions.
13. Familiarity with metadata management, master data management (MDM, and governance standards to ensure accuracy, compliance, and audit readiness.
14. Strong collaboration with data architects and solution designers to define ingestion patterns, integration approaches, and scalable architecture blueprints.
15. Demonstrated leadership in data modernization projects, mentoring junior engineers, and driving ingestion/migration initiatives with measurable outcomes such as reduced processing times, improved data quality, and successful legacy decommissioning.